Conditional Coding for Flexible Learned Video Compression

Théo Ladune (1, 2) , Pierrick Philippe (2) , Wassim Hamidouche (1) , Lu Zhang (1) , Olivier Déforges (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 Orange Labs

This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frames, allowing to process intra and inter frames with the same coder. The system is trained through the minimization of a rate-distortion cost, with no pre-training or proxy loss. Its flexibility is assessed under three coding configurations (All Intra, Low-delay P and Random Access), where it is shown to achieve performance competitive with the state-of-the-art video codec HEVC.

Mots clés: End-to-end; Deep Learning; Video Coding

Domaines: Traitement du signal et de l'image [eess.SP]; Réseau de neurones [cs.NE]; Traitement des images [eess.IV]

Théo Ladune, Pierrick Philippe, Wassim Hamidouche, Lu Zhang, Olivier Déforges. Conditional Coding for Flexible Learned Video Compression. International Conference on Learning Representations (ICLR) 2021, Neural Compression Workshop, May 2021, Vienne (virtual), Austria. ⟨hal-03192548v3⟩